Knowledge Retrieval

Perform semantic search over a pre-ingested document collection using Retrieval-Augmented Generation (RAG). Backed by LlamaIndex with ChromaDB or NVIDIA Foundational RAG.

When to Use

• Searching internal or pre-ingested documents and reports
• Finding information in PDFs, whitepapers, or technical documentation
• Retrieving domain-specific knowledge not available on the open web
• This is the highest priority source — check the knowledge base first before web or paper searches

How to Use

1. Formulate a semantic search query describing the information needed
2. Call knowledge_retrieval with the query
3. Review returned chunks for relevance
4. Note the citation metadata (filename, page number) for sourcing

Result Format

Results are returned as text chunks with citation metadata:

Relevant text passage from the ingested document...

Citation: filename.pdf, p.12

Constraints

• Searches only over documents that have been ingested into the knowledge index
• Returns ranked chunks based on semantic similarity
• Citation format: Citation: filename.ext, p.X
• Each call counts toward the researcher's 8-call limit per task

Backend Options

• LlamaIndex + ChromaDB — Local vector store with LlamaIndex orchestration
• NVIDIA Foundational RAG — NVIDIA-hosted RAG service with NeMo Retriever